Créer une nouvelle compétence

VérifiéSûr

Workflow complet pour créer une compétence IA structurée et documentée. Guide la conception adaptative, la recherche d'API externes et l'organisation modulaire.

Spar Skills Guide Bot
DeveloppementAvancé
3002/06/2026
Claude Code
#skill-creation#workflow-generation#ai-agent-skills#meta-skill

Recommandé pour

Notre avis

Cette méta-compétence guide l'utilisateur à travers un processus adaptatif pour créer de nouvelles compétences d'agent IA, depuis la collecte des exigences jusqu'à la structure des dossiers et la génération de contenu.

Points forts

  • Les questions adaptatives personnalisent la compétence selon les besoins spécifiques
  • Fournit des modèles de structure de fichiers clairs pour les compétences simples et complexes
  • L'étape de recherche intégrée pour les API externes garantit des implémentations précises
  • Construit des composants réutilisables comme workflows, références, templates et scripts

Limites

  • Nécessite une familiarité avec la structure de compétence recommandée et le format XML
  • Peut être excessif pour des compétences très simples qui pourraient être écrites rapidement à la main
  • Dépend d'outils externes comme Context7 MCP pour la recherche d'API
Quand l'utiliser

Utilisez cette compétence lorsque vous devez créer systématiquement une compétence d'agent IA bien structurée impliquant plusieurs workflows ou intégrations externes.

Quand l'éviter

N'utilisez pas cette compétence si vous ne faites qu'une petite instruction ponctuelle ou si vous êtes déjà à l'aise pour écrire un SKILL.md à la main.

Analyse de sécurité

Sûr
Score qualité85/100

The skill describes a legitimate workflow for creating new AI skills, involving directory and file creation via mkdir and cat. There are no destructive commands, data exfiltration, or safety bypasses. The operations are standard and user-intended.

Aucun point d'attention détecté

Exemples

Create a skill for Docker management
I want to build a new skill that helps manage Docker containers, images, and compose files. It should include common operations like status checks, logs, and basic troubleshooting.
Build a skill for code review automation
Create a skill for automated code review that checks for security issues, style violations, and provides feedback in pull requests.
Develop a skill for project scaffolding
Help me create a skill that scaffolds new projects with a standard folder structure, configuration files, and initial documentation.

Workflow: Create a New Skill

<required_reading> Read these reference files NOW:

  1. references/recommended-structure.md
  2. references/skill-structure.md
  3. references/core-principles.md
  4. references/use-xml-tags.md </required_reading>
<process> ## Step 1: Adaptive Requirements Gathering

If user provided context (e.g., "build a skill for X"): → Analyze what's stated, what can be inferred, what's unclear → Skip to asking about genuine gaps only

If user just invoked skill without context: → Ask what they want to build

Using question

Ask 2-4 domain-specific questions based on actual gaps. Each question should:

  • Have specific options with descriptions
  • Focus on scope, complexity, outputs, boundaries
  • NOT ask things obvious from context

Example questions:

  • "What specific operations should this skill handle?" (with options based on domain)
  • "Should this also handle [related thing] or stay focused on [core thing]?"
  • "What should the user see when successful?"

Decision Gate

After initial questions, ask: "Ready to proceed with building, or would you like me to ask more questions?"

Options:

  1. Proceed to building - I have enough context
  2. Ask more questions - There are more details to clarify
  3. Let me add details - I want to provide additional context

Step 2: Research Trigger (If External API)

When external service detected, ask using question: "This involves [service name] API. Would you like me to research current endpoints and patterns before building?"

Options:

  1. Yes, research first - Fetch current documentation for accurate implementation
  2. No, proceed with general patterns - Use common patterns without specific API research

If research requested:

  • Use Context7 MCP to fetch current library documentation
  • Or use WebSearch for recent API documentation
  • Focus on 2024-2026 sources
  • Store findings for use in content generation

Step 3: Decide Structure

Simple skill (single workflow, <200 lines): → Single SKILL.md file with all content

Complex skill (multiple workflows OR domain knowledge): → Router pattern:

skill-name/
├── SKILL.md (router + principles)
├── workflows/ (procedures - FOLLOW)
├── references/ (knowledge - READ)
├── templates/ (output structures - COPY + FILL)
└── scripts/ (reusable code - EXECUTE)

Factors favoring router pattern:

  • Multiple distinct user intents (create vs debug vs ship)
  • Shared domain knowledge across workflows
  • Essential principles that must not be skipped
  • Skill likely to grow over time

Consider templates/ when:

  • Skill produces consistent output structures (plans, specs, reports)
  • Structure matters more than creative generation

Consider scripts/ when:

  • Same code runs across invocations (deploy, setup, API calls)
  • Operations are error-prone when rewritten each time

See references/recommended-structure.md for templates.

Step 4: Create Directory

mkdir -p ~/.config/opencode/skills/{skill-name}
# If complex:
mkdir -p ~/.config/opencode/skills/{skill-name}/workflows
mkdir -p ~/.config/opencode/skills/{skill-name}/references
# If needed:
mkdir -p ~/.config/opencode/skills/{skill-name}/templates  # for output structures
mkdir -p ~/.config/opencode/skills/{skill-name}/scripts    # for reusable code

Step 5: Write SKILL.md

Simple skill: Write complete skill file with:

  • YAML frontmatter (name, description)
  • <objective>
  • <quick_start>
  • Content sections with pure XML
  • <success_criteria>

Complex skill: Write router with:

  • YAML frontmatter
  • <essential_principles> (inline, unavoidable)
  • <intake> (question to ask user)
  • <routing> (maps answers to workflows)
  • <reference_index> and <workflows_index>

Step 6: Write Workflows (if complex)

For each workflow:

<required_reading>
Which references to load for this workflow
</required_reading>

<process>
Step-by-step procedure
</process>

<success_criteria>
How to know this workflow is done
</success_criteria>

Step 7: Write References (if needed)

Domain knowledge that:

  • Multiple workflows might need
  • Doesn't change based on workflow
  • Contains patterns, examples, technical details

Step 8: Validate Structure

Check:

  • [ ] YAML frontmatter valid
  • [ ] Name matches directory (lowercase-with-hyphens)
  • [ ] Description says what it does AND when to use it (third person)
  • [ ] No markdown headings (#) in body - use XML tags
  • [ ] Required tags present: objective, quick_start, success_criteria
  • [ ] All referenced files exist
  • [ ] SKILL.md under 500 lines
  • [ ] XML tags properly closed

Step 9: Create Slash Command

cat > ~/.config/opencode/commands/{skill-name}.md << 'EOF'
---
description: {Brief description}
argument-hint: [{argument hint}]
allowed-tools: Skill({skill-name})
---

Invoke the {skill-name} skill for: $ARGUMENTS
EOF

Step 10: Test

Invoke the skill and observe:

  • Does it ask the right intake question?
  • Does it load the right workflow?
  • Does the workflow load the right references?
  • Does output match expectations?

Iterate based on real usage, not assumptions. </process>

<success_criteria> Skill is complete when:

  • [ ] Requirements gathered with appropriate questions
  • [ ] API research done if external service involved
  • [ ] Directory structure correct
  • [ ] SKILL.md has valid frontmatter
  • [ ] Essential principles inline (if complex skill)
  • [ ] Intake question routes to correct workflow
  • [ ] All workflows have required_reading + process + success_criteria
  • [ ] References contain reusable domain knowledge
  • [ ] Slash command exists and works
  • [ ] Tested with real invocation </success_criteria>
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